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<!DOCTYPE html>
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<meta property="og:title" content="11.1. Array API support (experimental)" />
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<li class="toctree-l1 has-children"><a class="reference internal" href="../supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="linear_model.html">1.1. Linear Models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="compose.html">6.1. Pipelines and composite estimators</a></li>
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<li class="toctree-l2"><a class="reference internal" href="unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
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<li class="toctree-l2"><a class="reference internal" href="kernel_approximation.html">6.7. Kernel Approximation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
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</details></li>
<li class="toctree-l1"><a class="reference internal" href="../model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="../common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="../dispatching.html">11. Dispatching</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2 current active"><a class="current reference internal" href="#">11.1. Array API support (experimental)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="../presentations.html">13. External Resources, Videos and Talks</a></li>
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<section id="array-api-support-experimental">
<span id="array-api"></span><h1><span class="section-number">11.1. </span>Array API support (experimental)<a class="headerlink" href="#array-api-support-experimental" title="Link to this heading">#</a></h1>
<p>The <a class="reference external" href="https://data-apis.org/array-api/latest/">Array API</a> specification defines
a standard API for all array manipulation libraries with a NumPy-like API.
Scikit-learn’s Array API support requires
<a class="reference external" href="https://github.com/data-apis/array-api-compat">array-api-compat</a> to be installed.</p>
<p>Some scikit-learn estimators that primarily rely on NumPy (as opposed to using
Cython) to implement the algorithmic logic of their <code class="docutils literal notranslate"><span class="pre">fit</span></code>, <code class="docutils literal notranslate"><span class="pre">predict</span></code> or
<code class="docutils literal notranslate"><span class="pre">transform</span></code> methods can be configured to accept any Array API compatible input
datastructures and automatically dispatch operations to the underlying namespace
instead of relying on NumPy.</p>
<p>At this stage, this support is <strong>considered experimental</strong> and must be enabled
explicitly as explained in the following.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Currently, only <code class="docutils literal notranslate"><span class="pre">cupy.array_api</span></code>, <code class="docutils literal notranslate"><span class="pre">array-api-strict</span></code>, <code class="docutils literal notranslate"><span class="pre">cupy</span></code>, and <code class="docutils literal notranslate"><span class="pre">PyTorch</span></code>
are known to work with scikit-learn’s estimators.</p>
</div>
<section id="example-usage">
<h2><span class="section-number">11.1.1. </span>Example usage<a class="headerlink" href="#example-usage" title="Link to this heading">#</a></h2>
<p>Here is an example code snippet to demonstrate how to use <a class="reference external" href="https://cupy.dev/">CuPy</a> to run
<a class="reference internal" href="generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearDiscriminantAnalysis</span></code></a> on a GPU:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">config_context</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">cupy</span>
<span class="gp">>>> </span><span class="n">X_np</span><span class="p">,</span> <span class="n">y_np</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_cu</span> <span class="o">=</span> <span class="n">cupy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_np</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y_cu</span> <span class="o">=</span> <span class="n">cupy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_np</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_cu</span><span class="o">.</span><span class="n">device</span>
<span class="go"><CUDA Device 0></span>
<span class="gp">>>> </span><span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">array_api_dispatch</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">lda</span> <span class="o">=</span> <span class="n">LinearDiscriminantAnalysis</span><span class="p">()</span>
<span class="gp">... </span> <span class="n">X_trans</span> <span class="o">=</span> <span class="n">lda</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_cu</span><span class="p">,</span> <span class="n">y_cu</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_trans</span><span class="o">.</span><span class="n">device</span>
<span class="go"><CUDA Device 0></span>
</pre></div>
</div>
<p>After the model is trained, fitted attributes that are arrays will also be
from the same Array API namespace as the training data. For example, if CuPy’s
Array API namespace was used for training, then fitted attributes will be on the
GPU. We provide a experimental <code class="docutils literal notranslate"><span class="pre">_estimator_with_converted_arrays</span></code> utility that
transfers an estimator attributes from Array API to a ndarray:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.utils._array_api</span> <span class="kn">import</span> <span class="n">_estimator_with_converted_arrays</span>
<span class="gp">>>> </span><span class="n">cupy_to_ndarray</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">array</span> <span class="p">:</span> <span class="n">array</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">lda_np</span> <span class="o">=</span> <span class="n">_estimator_with_converted_arrays</span><span class="p">(</span><span class="n">lda</span><span class="p">,</span> <span class="n">cupy_to_ndarray</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_trans</span> <span class="o">=</span> <span class="n">lda_np</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_np</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">X_trans</span><span class="p">)</span>
<span class="go"><class 'numpy.ndarray'></span>
</pre></div>
</div>
<section id="pytorch-support">
<h3><span class="section-number">11.1.1.1. </span>PyTorch Support<a class="headerlink" href="#pytorch-support" title="Link to this heading">#</a></h3>
<p>PyTorch Tensors are supported by setting <code class="docutils literal notranslate"><span class="pre">array_api_dispatch=True</span></code> and passing in
the tensors directly:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="n">X_torch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_np</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y_torch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_np</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">array_api_dispatch</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">lda</span> <span class="o">=</span> <span class="n">LinearDiscriminantAnalysis</span><span class="p">()</span>
<span class="gp">... </span> <span class="n">X_trans</span> <span class="o">=</span> <span class="n">lda</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_torch</span><span class="p">,</span> <span class="n">y_torch</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">X_trans</span><span class="p">)</span>
<span class="go"><class 'torch.Tensor'></span>
<span class="gp">>>> </span><span class="n">X_trans</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span>
<span class="go">'cuda'</span>
</pre></div>
</div>
</section>
</section>
<section id="support-for-array-api-compatible-inputs">
<span id="array-api-supported"></span><h2><span class="section-number">11.1.2. </span>Support for <code class="docutils literal notranslate"><span class="pre">Array</span> <span class="pre">API</span></code>-compatible inputs<a class="headerlink" href="#support-for-array-api-compatible-inputs" title="Link to this heading">#</a></h2>
<p>Estimators and other tools in scikit-learn that support Array API compatible inputs.</p>
<section id="estimators">
<h3><span class="section-number">11.1.2.1. </span>Estimators<a class="headerlink" href="#estimators" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><a class="reference internal" href="generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">decomposition.PCA</span></code></a> (with <code class="docutils literal notranslate"><span class="pre">svd_solver="full"</span></code>,
<code class="docutils literal notranslate"><span class="pre">svd_solver="randomized"</span></code> and <code class="docutils literal notranslate"><span class="pre">power_iteration_normalizer="QR"</span></code>)</p></li>
<li><p><a class="reference internal" href="generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge" title="sklearn.linear_model.Ridge"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.Ridge</span></code></a> (with <code class="docutils literal notranslate"><span class="pre">solver="svd"</span></code>)</p></li>
<li><p><a class="reference internal" href="generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">discriminant_analysis.LinearDiscriminantAnalysis</span></code></a> (with <code class="docutils literal notranslate"><span class="pre">solver="svd"</span></code>)</p></li>
<li><p><a class="reference internal" href="generated/sklearn.preprocessing.KernelCenterer.html#sklearn.preprocessing.KernelCenterer" title="sklearn.preprocessing.KernelCenterer"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.KernelCenterer</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler" title="sklearn.preprocessing.MaxAbsScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.MaxAbsScaler</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler" title="sklearn.preprocessing.MinMaxScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.MinMaxScaler</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.preprocessing.Normalizer.html#sklearn.preprocessing.Normalizer" title="sklearn.preprocessing.Normalizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.Normalizer</span></code></a></p></li>
</ul>
</section>
<section id="metrics">
<h3><span class="section-number">11.1.2.2. </span>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><a class="reference internal" href="generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score" title="sklearn.metrics.accuracy_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.accuracy_score</span></code></a></p></li>
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.cosine_similarity`</span></code></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.r2_score</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.zero_one_loss.html#sklearn.metrics.zero_one_loss" title="sklearn.metrics.zero_one_loss"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.zero_one_loss</span></code></a></p></li>
</ul>
</section>
<section id="tools">
<h3><span class="section-number">11.1.2.3. </span>Tools<a class="headerlink" href="#tools" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><a class="reference internal" href="generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split"><code class="xref py py-func docutils literal notranslate"><span class="pre">model_selection.train_test_split</span></code></a></p></li>
</ul>
<p>Coverage is expected to grow over time. Please follow the dedicated <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/22352">meta-issue on GitHub</a> to track progress.</p>
</section>
<section id="type-of-return-values-and-fitted-attributes">
<h3><span class="section-number">11.1.2.4. </span>Type of return values and fitted attributes<a class="headerlink" href="#type-of-return-values-and-fitted-attributes" title="Link to this heading">#</a></h3>
<p>When calling functions or methods with Array API compatible inputs, the
convention is to return array values of the same array container type and
device as the input data.</p>
<p>Similarly, when an estimator is fitted with Array API compatible inputs, the
fitted attributes will be arrays from the same library as the input and stored
on the same device. The <code class="docutils literal notranslate"><span class="pre">predict</span></code> and <code class="docutils literal notranslate"><span class="pre">transform</span></code> method subsequently expect
inputs from the same array library and device as the data passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code>
method.</p>
<p>Note however that scoring functions that return scalar values return Python
scalars (typically a <code class="docutils literal notranslate"><span class="pre">float</span></code> instance) instead of an array scalar value.</p>
</section>
</section>
<section id="common-estimator-checks">
<h2><span class="section-number">11.1.3. </span>Common estimator checks<a class="headerlink" href="#common-estimator-checks" title="Link to this heading">#</a></h2>
<p>Add the <code class="docutils literal notranslate"><span class="pre">array_api_support</span></code> tag to an estimator’s set of tags to indicate that
it supports the Array API. This will enable dedicated checks as part of the
common tests to verify that the estimators result’s are the same when using
vanilla NumPy and Array API inputs.</p>
<p>To run these checks you need to install
<a class="reference external" href="https://github.com/data-apis/array-api-compat">array_api_compat</a> in your
test environment. To run the full set of checks you need to install both
<a class="reference external" href="https://pytorch.org/">PyTorch</a> and <a class="reference external" href="https://cupy.dev/">CuPy</a> and have
a GPU. Checks that can not be executed or have missing dependencies will be
automatically skipped. Therefore it’s important to run the tests with the
<code class="docutils literal notranslate"><span class="pre">-v</span></code> flag to see which checks are skipped:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><style type="text/css">
span.prompt1:before {
content: "$ ";
}
</style><span class="prompt1">pip<span class="w"> </span>install<span class="w"> </span>array-api-compat<span class="w"> </span><span class="c1"># and other libraries as needed</span></span>
<span class="prompt1">pytest<span class="w"> </span>-k<span class="w"> </span><span class="s2">"array_api"</span><span class="w"> </span>-v</span>
</pre></div></div><section id="note-on-mps-device-support">
<h3><span class="section-number">11.1.3.1. </span>Note on MPS device support<a class="headerlink" href="#note-on-mps-device-support" title="Link to this heading">#</a></h3>
<p>On macOS, PyTorch can use the Metal Performance Shaders (MPS) to access
hardware accelerators (e.g. the internal GPU component of the M1 or M2 chips).
However, the MPS device support for PyTorch is incomplete at the time of
writing. See the following github issue for more details:</p>
<ul class="simple">
<li><p><a class="github reference external" href="https://github.com/pytorch/pytorch/issues/77764">pytorch/pytorch#77764</a></p></li>
</ul>
<p>To enable the MPS support in PyTorch, set the environment variable
<code class="docutils literal notranslate"><span class="pre">PYTORCH_ENABLE_MPS_FALLBACK=1</span></code> before running the tests:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1"><span class="nv">PYTORCH_ENABLE_MPS_FALLBACK</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>pytest<span class="w"> </span>-k<span class="w"> </span><span class="s2">"array_api"</span><span class="w"> </span>-v</span>
</pre></div></div><p>At the time of writing all scikit-learn tests should pass, however, the
computational speed is not necessarily better than with the CPU device.</p>
</section>
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